The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating contextually relevant transcripts of voice recordings based on social networking data. For instance, the disclosed systems receive a voice recording from a user corresponding to a message thread including the user and one or more co-users. The disclosed systems analyze acoustic features of the voice recording to generate transcription-text probabilities. The disclosed systems generate term weights for terms corresponding to objects associated with the user within a social networking system by analyzing user social networking data. Using the contextually aware term weights, the disclosed systems adjust the transcription-text probabilities. Based on the adjusted transcription-text probabilities, the disclosed systems generate a transcript of the voice recording for display within the message thread.
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2. The method of claim 1, further comprising generate the priority weights to indicate a likelihood of the user speaking particular terms.
3. The method of claim 1, further comprising determining a term weight for a term utilizing a historical term utilized by the user and the one or more co-users within the message thread for the user and the one or more co-users.
4. The method of claim 1, further comprising determining a term weight for a term utilizing a frequency with which the user and the one or more co-users mention the historical terms within the one or more message threads.
11. The non-transitory computer readable medium as recited in claim 10, further comprising instructions that, when executed by the at least one processor, cause the computer system to generate the priority weights to indicate a likelihood of the user speaking particular terms.
12. The non-transitory computer readable medium as recited in claim 10, further comprising instructions that, when executed by the at least one processor, cause the computer system to determine a term weight for a term utilizing a historical term utilized by the user and the one or more co-users within the message thread for the user and the one or more co-users.
13. The non-transitory computer readable medium as recited in claim 10, further comprising instructions that, when executed by the at least one processor, cause the computer system to determine a term weight for a term utilizing a frequency with which the user and the one or more co-users mention the historical terms within the one or more message threads.
16. The system as recited in claim 15, further comprising instructions that, when executed by the at least one processor, cause the system to generate the priority weights to indicate a likelihood of the user speaking particular terms.
17. The system as recited in claim 15, further comprising instructions that, when executed by the at least one processor, cause the system to determine a term weight for a term utilizing a historical term utilized by the user and the one or more co-users within the message thread for the user and the one or more co-users.
18. The system as recited in claim 15 further comprising instructions that, when executed by the at least one processor, cause the system to determine a term weight for a term utilizing a frequency with which the user and the one or more co-users mention the historical terms within the one or more message threads.
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October 28, 2019
March 21, 2023
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